Cluster Around Latent Variable for Vulnerability Towards Natural Hazards, Non-Natural Hazards, Social Hazards in West Papua

Caraka, Rezzy Eko; Lee, Youngjo; Chen, Rung Ching; Toharudin, Toni; Gio, Prana Ugiana; Kurniawan, Robert; Pardamean, Bens

doi:10.1109/access.2020.3038883

Cited by 41 publications

(30 citation statements)

References 58 publications

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“…The consequences of landslide occurrences on substations/power plants are evaluated using (2) and landslide damage risk has been assessed by (3). Results show that the average hazard index at the location of generation facilities are negligible and their operation are not threatened by a landslide event.…”

Section: Resultsmentioning

confidence: 99%

“…As a result, power systems are highly vulnerable to natural catastrophes as geographically dispersed components of interconnected power systems are subjected to a wide range of natural hazards. Considering the importance of electricity in the well-being of modern societies, the vulnerability of electrical infrastructure to natural hazards, and recent natural events such as hurricane sandy, resilience studies of power systems have gained significant attention in recent years [2][3][4]. In this context, resilience is defined as "the ability to prepare for and adapt to the changing conditions as well as withstand and recover rapidly from disruptions" [5].…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Quantification of Power System Resilience to Natural Hazards: A Case of Landslide

et al. 2021

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Power systems are stretched across thousands of miles of diverse territories, often in remote locations, to generate and transfer the energy to geographically dispersed customers. The system is therefore subjected to a wide range of natural hazards which could potentially damage critical system components and cause interruption of electricity supply in some areas. To improve system resilience against natural hazards, management frameworks are required to identify hazardous areas and prioritize reinforcement activities in order to take the most out of the limited resources.Landslide is a natural disaster that involves the breakup and downhill flow of rock, mud, water, and anything caught in the path. It is a phenomenon frequently occurred in some parts of the world that could result in the failure of power transmission networks. Consequently, in this paper, a novel approach has been proposed that quantifies the landslide hazard, its damage to power system components, and the impacts on the overall system performance to prioritize reinforcement activities and mitigate the landslide vulnerability. The proposed approach is applied to a real power system and the obtained results are discussed in detail.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Quantification of Power System Resilience to Natural Hazards: A Case of Landslide

et al. 2021

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“…GIS-driven spatial analysis can facilitate the combination between health data and characteristic of spatial attributes. Descriptive modeling research that took advantage of those strength has deeply exposed the spatial-temporal associations of COVID-19 with socioeconomic and environmental characteristics [ 14 , 15 ]. However, as far as an engaging empirical study, it is important to select variables that reveal the degree of social vulnerability [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties

Zhang

Chow

2021

IJERPH

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As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental 14 impact’s indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, age structure, natural supply, economic condition, air quality, and medical care. We established the GWR model to seek the sensitive factors. The result shows that population, hospitalization, and economic condition are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).

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“…GIS-driven spatial analysis can facilitate the combination between health data and characteristic of spatial attributes. Descriptive modeling research that took advantage of those strength has deeply exposed the spatial-temporal associations of COVID-19 with socioeconomic and environmental characteristics [14][15]. However, as far as an engaging empirical study, it is important to select variables that reveal the degree of social vulnerability [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality Impacts on Texas Counties

Zhang

Wu²,

Chow

2021

Preprint

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As COVID-19 run rampant in high-density housing sites, it is important to use real-time data tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research is appropriately analyzed the disparities between spatial-temporal clusters, expectation Maximization clustering (EM) and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to South-east Texas analysis in GWR modeling. The sensitive period took place in the last two quarters in 2020. We explored Postgre application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental 14 impact’s indices to perform Principal Component Analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, age structure, natural supply, economic condition, air quality and medical care. We established the GWR model to seek the sensitive factors. The result shows that population, hospitalization, and economic condition are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility and satisfy for the need of Emergency Operations Plan (EOP).

show abstract

Cluster Around Latent Variable for Vulnerability Towards Natural Hazards, Non-Natural Hazards, Social Hazards in West Papua

Cited by 41 publications

References 58 publications

Modeling and Quantification of Power System Resilience to Natural Hazards: A Case of Landslide

Modeling and Quantification of Power System Resilience to Natural Hazards: A Case of Landslide

Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties

Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality Impacts on Texas Counties

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